Developing A Prosthetic, Healthy, And Fill-In Detection Model from X-Ray Dental Images Using Modern Electronic Computing Machines

Abstract

This paper presents a novel deep learning model for detecting prosthetic, healthy, and fill-in regions in dental X- ray images using modern electronic computing machines. The model achieves state-of-the-art performance in accurate and efficient detection of dental structures, assisting dentists in diagnosis and treatment planning. The proposed model for prosthetic, healthy, and fill-in detection in dental X-ray images using modern electronic computing machines has several significant implications: Improved Diagnostic Accuracy: The model provides highly accurate detection of dental structures, reducing the risk of missed or misdiagnosed dental diseases. Enhanced Treatment Planning: The model assists dentists in developing more precise and effective treatment plans by providing detailed information about the location and extent of dental structures. The proposed model represents a significant advancement in the field of dental image analysis. It provides a powerful tool for automated detection of dental structures in X- ray images, aiding dentists in diagnosis, treatment planning, and patient communication. Further research will focus on exploring the model's applications in other areas of dentistry, such as caries detection and periodontal disease assessment.